Many people still associate artificial intelligence with science fiction dystopias, but that characterization is waning as artificial intelligence develops and becomes more commonplace in our daily lives. Today, artificial intelligence is a household name (and sometimes even a household presence – hi, Alexa!).

While artificial intelligence’s acceptance in mainstream society is a new phenomenon, it is not a new concept. The modern field of artificial intelligence came into existence in 1956, but it took decades of work to make significant progress toward developing an artificial intelligence system and making it a technological reality.

In business, artificial intelligence has a wide range of uses. In fact, most of us interact with artificial intelligence in some form or another on a daily basis. From the mundane to the breathtaking, artificial intelligence is already disrupting virtually every business process in every industry. As artificial intelligence technologies proliferate, they are becoming an imperative for businesses that want to maintain a competitive edge.

What is artificial intelligence?

by: Adam C. Uzialko

Before examining how artificial intelligence technologies are impacting the business world, it’s important to define the term. “Artificial intelligence” is a broad and general term that refers to any type of computer software that engages in humanlike activities, including learning, planning and problem-solving. Calling specific applications “artificial intelligence” is like calling a 2013 Honda Accord a “vehicle” – it’s technically correct, but it doesn’t cover any of the specifics. To understand what type of artificial intelligence is predominant in business, we have to dig deeper.

Machine learning

Machine learning is one of the most common types of artificial intelligence in development for business purposes today. Machine learning is primarily used to process large amounts of data quickly. These types of artificial intelligence are algorithms that appear to “learn” over time, getting better at what they do the more often they do it. Feed a machine learning algorithm more data and its modeling should improve. Machine learning is useful for putting vast troves of data – increasingly captured by connected devices and the internet of things – into a digestible context for humans.

For example, if you manage a manufacturing plant, your machinery is likely hooked up to the network. Connected devices feed a constant stream of data about functionality, production and more to a central location. Unfortunately, it’s too much data for a human to ever sift through, and even if they could, they would likely miss most of the patterns. Machine learning can rapidly analyze the data as it comes in, identifying patterns and anomalies. If a machine in the manufacturing plant is working at a reduced capacity, a machine learning algorithm can catch it and notify decision-makers that it’s time to dispatch a preventive maintenance team.

But machine learning is also a relatively broad category. The development of artificial neural networks, an interconnected web of artificial intelligence “nodes,” has given rise to what is known as “deep learning.”

Deep learning

Deep learning is an even more specific version of machine learning that relies on neural networks to engage in nonlinear reasoning. Deep learning is critical to performing more advanced functions, such as fraud detection. It can do this by analyzing a wide range of factors at once. For example, for self-driving cars to work, several factors must be identified, analyzed and responded to at once. Deep learning algorithms are used to help self-driving cars contextualize information picked up by their sensors, like the distance of other objects, the speed at which they are moving and a prediction of where they will be in 5-10 seconds. All this information is calculated side by side to help a self-driving car make decisions like when to change lanes.

Deep learning has a great deal of promise in business and is likely to be more commonly used soon. Older machine learning algorithms tend to plateau in their capability once a certain amount of data has been captured, but deep learning models continue to improve their performance as more data is received. This makes deep learning models far more scalable and detailed; you could even say deep learning models are far more independent.

Artificial intelligence and business today

Rather than serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Although artificial intelligence currently has a difficult time completing commonsense tasks in the real world, it is adept at processing and analyzing troves of data far more quickly than a human brain could. Artificial intelligence software can then return with synthesized courses of action and present them to the human user. In this way, humans can use artificial intelligence to help game out possible consequences of each action and streamline the decision-making process.

“Artificial intelligence is kind of the second coming of software,” said Amir Husain, founder and CEO of machine learning company SparkCognition. “It’s a form of software that makes decisions on its own, that’s able to act even in situations not foreseen by the programmers. Artificial intelligence has a wider latitude of decision-making ability as opposed to traditional software.”

Those traits make artificial intelligence highly valuable throughout many industries, whether it’s simply helping visitors and staff make their way around a corporate campus efficiently or performing a task as complex as monitoring a wind turbine to predict when it will need repairs.

Machine learning is used often in systems that capture vast amounts of data. For example, smart energy management systems collect data from sensors affixed to various assets. The troves of data are then contextualized by machine learning algorithms and delivered to human decision-makers to better understand energy usage and maintenance demands.

Artificial intelligence is even an indispensable ally when it comes to looking for holes in computer network defenses, Husain said.

“You really can’t have enough cybersecurity experts to look at these problems, because of scale and increasing complexity,” he said. “Artificial intelligence is playing an increasing role here as well.”

Artificial intelligence is also changing customer relationship management (CRM) systems. Software like Salesforce or Zoho requires heavy human intervention to remain up to date and accurate. But when you apply artificial intelligence to these platforms, a normal CRM system transforms into a self-updating, auto-correcting system that stays on top of your relationship management for you. [For those in brand-new companies, read our report on CRM tools for startups.]

Another example of artificial intelligence’s versatility is within the financial sector. Dr. Hossein Rahnama, founder and CEO of artificial intelligence concierge company Flybits and visiting professor at the Massachusetts Institute of Technology, worked with TD Bank to integrate artificial intelligence into regular banking operations, such as mortgage loans.

“Using this technology, if you have a mortgage with the bank and it’s up for renewal in 90 days or less … if you’re walking by a branch, you get a personalized message inviting you to go to the branch and renew purchase,” Rahnama said. “If you’re looking at a property for sale and you spend more than 10 minutes there, it will send you a possible mortgage offer.

“We’re no longer expecting the user to constantly be on a search box Googling what they need,” he added. “The paradigm is shifting as to how the right information finds the right user at the right time.”

The future of artificial intelligence

So, how might artificial intelligence be used in the future? It’s hard to say how the technology will develop, but most experts see those “commonsense” tasks becoming even easier for computers to process. That means robots will become extremely useful in day-to-day life.

“AI is starting to make what was once considered impossible possible, like driverless cars,” said Russell Glenister, CEO and founder of Curation Zone. “Driverless cars are only a reality because of access to training data and fast GPUs, which are both key enablers. To train driverless cars, an enormous amount of accurate data is required, and speed is key to undertake the training. Five years ago, the processors were too slow, but the introduction of GPUs made it all possible.”

Glenister said GPUs are only going to get faster, improving the applications of artificial intelligence software across the board.

“Fast processes and lots of clean data are key to the success of AI,” he said.

Other analysts, like co-founder and CTO of Nara Logics Dr. Nathan Wilson, said they see artificial intelligence on the cusp of revolutionizing familiar activities, such as dining. Wilson predicted that artificial intelligence could be used by a restaurant, for example, to decide which music to play based on the interests of the guests in attendance. Artificial intelligence could even alter the appearance of the wallpaper based on what the technology anticipates the aesthetic preferences of the crowd might be.

If that isn’t far-out enough for you, Rahnama predicted that artificial intelligence will take digital technology out of the two-dimensional, screen-imprisoned form to which people have grown accustomed. Instead, the primary user interface will become the physical environment surrounding an individual.

“We’ve always relied on a two-dimensional display to play a game or interact with a webpage or read an e-book,” Rahnama said. “What’s going to happen now with artificial intelligence and a combination of [the internet of things] is that the display won’t be the main interface – the environment will be. You’ll see people designing experiences around them, whether it’s in connected buildings or connected boardrooms. These will be 3D experiences you can actually feel.”

What does artificial intelligence mean for the worker?

With all these new artificial intelligence use cases comes the daunting question of whether machines will force humans into obsolescence. The jury is still out: Some experts vehemently deny that artificial intelligence will automate so many jobs that millions of people find themselves unemployed, while other experts see it as a pressing problem.

“The structure of the workforce is changing, but I don’t think artificial intelligence is essentially replacing jobs,” Rahnama said. “It allows us to really create a knowledge-based economy and leverage that to create better automation for a better form of life. It might be a little bit theoretical, but I think if you have to worry about artificial intelligence and robots replacing our jobs, it’s probably algorithms replacing white-collar jobs such as business analysts, hedge fund managers and lawyers.”

Wilson said the shift toward artificial intelligence-based systems will likely cause the economy to add jobs that facilitate the transition.

“Artificial intelligence will create more wealth than it destroys,” Wilson said, “but it will not be equitably distributed, especially at first. The changes will be subliminally felt and not overt. [For example,] a tax accountant won’t one day receive a pink slip and meet the robot that is now going to sit at her desk. Rather, the next time the tax accountant applies for a job, it will be a bit harder to find one.”

Wilson said he anticipates that artificial intelligence in the workplace will fragment long-standing workflows, creating many human jobs to integrate those workflows. Other experts, like Husain, are not as sure about where the new jobs will come from once artificial intelligence becomes ubiquitous.

“[In the past,] there were opportunities to move from farming to manufacturing to services,” Husain said. “Now, that’s not the case. Why? Industry has been completely robotized, and we see that automation makes more sense economically.”

Husain pointed to self-driving trucks and artificial intelligence concierges like Siri and Cortana as examples, stating that as these technologies improve, widespread use could eliminate as many as 8 million jobs in the U.S. alone.

“When all these jobs start going away, we need to ask, ‘What is it that makes us productive? What does productivity mean?'” Husain said. “Now we’re confronting the changing reality and questioning society’s underlying assumptions. We must really think about this and decide what makes us productive and what is the value of people in society. We need to have this debate and have it quickly, because the technology won’t wait for us.”

Whether rosy or rocky, the future is coming quickly, and artificial intelligence will certainly be a part of it. As this technology develops, the world will see new startups, numerous business applications and consumer uses, as well as the displacement of certain jobs and the creation of entirely new ones. Along with the internet of things, artificial intelligence has the potential to dramatically remake the economy, but its exact impact remains to be seen.